Data-Driven Archives - gettectonic.com
data cloud and data silos
20Mar

Unify Your Data Silos

Unify Your Data Silos: Deliver Connected, Personalized Experiences 🔹 65% of customers expect businesses to tailor experiences to their evolving needs. (State of Data and Analytics Report, 2023) Have you ever received a marketing promotion for something you just bought? Or had to repeat your purchase history before getting help from customer service? These disjointed experiences frustrate customers and result from data silos that prevent a unified view of the customer journey. With enterprises using over 1,000 applications on average, data ecosystems are highly complex. Many businesses attempt to centralize data in lakes, warehouses, or lakehouses, yet 73% of enterprise data remains unused for analytics (Forrester). Why? Because much of this data stays locked in backend systems, failing to power the real-time applications and workflows that drive customer engagement and business success. Break Down Data Silos to Create Seamless Customer Experiences By unifying your data, you can personalize every interaction—from online reviews and service records to browsing history and purchases. And by securely activating this data within your CRM and AI-driven workflows, you can deliver smarter, faster, and more impactful customer experiences. Unlock Business Growth with Unified Data When your teams have access to a complete, real-time customer profile, they can turn insights into action across every touchpoint: ✅ Sales Teams receive real-time guidance during calls, offering tailored recommendations based on customer behavior.✅ Service Agents proactively address issues with instant alerts and AI-powered resolutions.✅ Marketers deliver personalized, cross-channel messaging, adapting dynamically to customer actions.✅ Retailers optimize shopping experiences by responding in real time to cart abandonment and browsing patterns.✅ IT Teams build real-time apps to detect fraud, assess economic trends, and enhance security. Checklist: Build a 360° View of Your Customer ✅ Basic Data: Demographics, job title, email, and IP address.✅ Interaction Data: Email opens, website visits, CTRs, customer service calls, and social media activity.✅ Behavioral & Attitudinal Data: Purchase history, order values, survey feedback, and online reviews. Turn Raw Data Into Actionable Insights with Data Cloud Data Cloud transforms fragmented data into a single, trusted source of truth, deeply integrated with the Salesforce Platform. It enables organizations to: ✔ Connect and unify all customer data without complex data pipelines.✔ Activate insights in real-time across sales, service, and marketing workflows.✔ Power trusted AI solutions using all enterprise data for better decision-making. See How Formula 1 Uses Data to Drive Fan Engagement 📺 Watch how F1 personalizes fan experiences “With over 500 million fans worldwide, we needed personalized journeys and always-on capabilities.”— Matthew Kemp, Senior CRM & Customer Operations Manager, F1 Checklist: Unite Your Data for Better Customer Experiences ✔ Make data easy to store, manage, and analyze from any source.✔ Think holistically about behavioral, interaction, and attitudinal data.✔ Use a platform that transforms raw data into actionable insights.✔ Audit your systems for data silos causing disconnected experiences.✔ Ensure data is accessible in everyday workflows with trusted solutions like Data Cloud. 📊 Data-driven businesses don’t just manage customer experiences—they revolutionize them. 🚀 Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 20, 2025in Data, Enterprise, Salesforce Data Cloud0 Comments
advanced analytics
18Mar

Maximizing Sales Performance with Salesforce Sales Analytics

Salesforce, a leading CRM platform, provides powerful sales analytics tools that empower businesses to make data-driven decisions, boost productivity, and drive revenue growth. This guide highlights the importance of Salesforce Sales Analytics, its key features, and how to leverage them to optimize your sales team’s performance. Key Features of Salesforce Sales Analytics 1. Reports and Dashboards Salesforce’s reporting capabilities enable users to create custom dashboards and reports for real-time insights into lead conversion, sales performance, and other critical metrics. These visual tools help businesses track trends and make informed decisions effortlessly. 2. Einstein Analytics Powered by AI and machine learning, Einstein Analytics offers: 3. Sales Cloud Analytics Sales Cloud Analytics helps businesses: 4. Forecasting Tools Salesforce’s AI-powered forecasting tools provide accurate revenue projections by analyzing: How Different Teams Benefit from Sales Analytics ✅ Executives – Gain a high-level view of sales, service, and pipeline performance with real-time dashboards highlighting key business metrics. ✅ Sales Managers – Monitor team performance, track quota attainment, analyze pipeline changes, and optimize sales cycles to accelerate deal closures. ✅ Sales Representatives – Get insights into personal sales performance, pipeline activities, and quota attainment, allowing for quicker decision-making and identification of new business opportunities. ✅ Operations Teams – Analyze sales performance by customer, region, and source to optimize negotiation strategies and business development efforts. Setting Up the Salesforce Sales Analytics App 1. Launch Analytics Studio Navigate to Analytics Studio via the Sales Home page and click on the Apps button. 2. Search for Sales Analytics If the Sales Analytics App does not appear by default, use the search bar to locate it. 3. Access the Default Dashboard The default dashboard provides key metrics, including: 4. Customize Widgets Modify data representation by selecting the Edit button and customizing widgets to align with your business needs. 5. Adjust Goal Widgets Update goal widgets to match your sales targets and business objectives. 6. Explore Additional Dashboards Salesforce offers role-specific dashboards for Sales Managers, Executives, and other stakeholders to gain tailored insights. 7. Utilize the Lens Feature for Visualizations Leverage Lens to create object-specific visualizations, such as opportunities by time, stage, owner, and more. Conclusion Integrating Salesforce Sales Analytics with Tableau CRM creates a comprehensive Business Intelligence (BI) solution. However, businesses looking for a simpler or more cost-effective alternative may consider exporting Salesforce data into third-party analytics platforms for additional flexibility and ease of use. By leveraging Salesforce Sales Analytics, businesses can enhance forecasting, improve decision-making, and drive sales performance with real-time insights. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 18, 2025in Data0 Comments
Salesforce Sales Engagement for Nonprofits
17Mar

Salesforce Sales Engagement for Nonprofits

Supercharge Your Nonprofit’s Donor Outreach with Salesforce Sales Engagement It’s time for the annual donor campaign. The fundraising team is juggling hundreds—if not thousands—of emails, phone calls, and follow-ups. The budget depends on surpassing last year’s donations, but with limited staff and time, reaching every potential donor feels impossible. The executive director asks, “How can we maximize donor engagement without increasing headcount—or at least focus our resources on the most likely givers?” Sound familiar? Every nonprofit faces the challenge of doing more with less. Fortunately, Salesforce offers a solution to keep your team organized, on task, and working smarter: Salesforce Sales Engagement. What Is Salesforce Sales Engagement? Salesforce Sales Engagement acts as a high-powered assistant for fundraising, membership, or program teams. This productivity hub, embedded within Salesforce, streamlines daily activities through automation, organization, and AI-powered insights. Key benefits include:✅ Cadences & Work Queues – Ensure no email, call, or task slips through the cracks.✅ Centralized Outreach – Keep all donor interactions in one place for seamless engagement.✅ AI-Driven Optimization – Analyze performance and refine strategies based on data. Previously known as High-Velocity Sales, Sales Engagement accelerates development cycles and scales outreach efforts effortlessly. By prioritizing critical tasks and automating others, your team can focus on building meaningful relationships and securing commitments faster. Engagement Cadences: Your Fundraising Playbook in Action Cadences serve as step-by-step roadmaps, guiding teams on how and when to engage with donors, members, or program participants. Whether you’re running a donor drive, promoting event registrations, or managing renewals, cadences ensure consistency and efficiency. Two Types of Cadences: 1️⃣ Standard Cadences – A structured sequence of touchpoints (emails, calls, LinkedIn messages) over time, such as a 30-day donor onboarding journey.2️⃣ Quick Cadences – A single, repeatable action, like automatically sending a thank-you email after a donation. With Cadence Builder, customizing workflows for different donor segments is fast and easy. Actions dynamically adjust based on engagement—so if a donor opens an email, they might receive a follow-up call; if they don’t, a reminder email is sent instead. Best of all, your team doesn’t have to track or remember each step—Sales Engagement assigns tasks automatically to individual work queues, ensuring nothing gets missed. Data-Driven Decision-Making with Built-In Analytics To improve outreach, you need to measure it. Sales Engagement provides powerful dashboards and reports to evaluate performance and optimize your approach. ✅ Track email open rates, call response rates, and donor engagement trends.✅ Identify successful messaging and refine underperforming campaigns.✅ Tie engagement metrics to fundraising goals to measure real impact. Unlike basic email tracking, Sales Engagement helps nonprofits connect data-driven insights with mission outcomes—empowering smarter decision-making without complex process changes. Already Using Salesforce Flows for Outreach? If your nonprofit relies on Salesforce Flow for outreach, you may wonder if Sales Engagement is necessary. The answer? Yes—if you want to do more with less effort. While flows are great for automation, Sales Engagement goes further with built-in call scripts, AI-powered task prioritization, and adaptable cadences. It offers a more intuitive, flexible approach to donor engagement, reducing manual work and boosting efficiency. Ready to Transform Your Fundraising Strategy? Salesforce Sales Engagement isn’t just for corporations—it’s a game-changer for nonprofits, helping teams increase donations, memberships, and event participation with less effort and greater impact. Are you ready to work smarter, engage better, and raise more? Let’s get started. Contact Tectonic today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 17, 2025in Data, Salesforce, Salesforce Flow0 Comments
Salesforce Energy and Utilities Cloud Explained
17Mar

Salesforce Energy and Utilities Cloud Explained

Salesforce Energy and Utilities Cloud is a specialized industry solution built on the Salesforce platform, designed to meet the unique needs of energy and utility companies. It helps these organizations manage customer relationships, streamline operations, and drive digital transformation in an increasingly complex and regulated industry. The cloud solution provides tools for customer engagement, field service management, and regulatory compliance, among other functionalities. Key Features of Salesforce Energy and Utilities Cloud: Benefits of Salesforce Energy and Utilities Cloud: Use Cases: Implementation Considerations: In summary, Salesforce Energy and Utilities Cloud is a comprehensive solution designed to help energy and utility companies enhance customer engagement, streamline operations, and achieve regulatory compliance. By leveraging advanced technologies and industry-specific functionalities, utilities can drive digital transformation and improve overall performance. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 17, 2025in Data, Salesforce, Salesforce Einstein0 Comments
ai arms race
15Mar

The Two Types of Voice AI

The Two Types of Voice AI: Assistive AI vs. Autonomous AI Voice AI is transforming customer service by automating tasks, enhancing productivity, and improving customer satisfaction. But not all Voice AI functions the same way — there are two primary types: Assistive AI and Autonomous AI. Understanding their unique roles can help businesses deploy the right solution to optimize efficiency, reduce costs, and deliver exceptional customer experiences. Assistive AI: Empowering Service Representatives Assistive AI works alongside human service representatives, enhancing their efficiency by providing real-time guidance and support during live interactions. Rather than replacing human agents, Assistive AI streamlines workflows, surfaces relevant information, and handles routine tasks — allowing service reps to resolve issues faster and more accurately. Here’s how Assistive AI transforms the customer service experience: 🚀 Real-Time Call Guidance As a customer describes their issue, Assistive AI follows the live call transcript, instantly surfacing relevant knowledge articles, past interaction history, and next-best actions for the agent. This eliminates the need for reps to manually search for information, reducing call times and improving resolution accuracy. For example, if a customer calls to reschedule a hotel stay, Assistive AI can immediately: The result? Faster resolutions and happier customers. 📝 Automated Call Summaries Generative AI capabilities allow Assistive AI to automatically summarize calls once they conclude. Instead of requiring agents to manually document case notes, Assistive AI generates: This significantly reduces post-call administrative work and ensures accurate case documentation. 🎯 Next-Best Action Recommendations Assistive AI can analyze customer sentiment and intent during a call. For example: This proactive support helps agents resolve issues faster, reduce churn, and improve overall customer satisfaction. 📊 Supervisor Alerts Based on Sentiment Assistive AI doesn’t just assist agents — it also helps supervisors. If Assistive AI detects a sharp decline in customer sentiment (such as anger, frustration, or confusion), it can: This prevents escalations from spiraling out of control, protecting the customer experience. ✅ Key Benefits of Assistive AI: Assistive AI empowers human agents — making them smarter, faster, and more effective at delivering outstanding customer service. Autonomous AI: Self-Sufficient Customer Service Agents While Assistive AI works alongside human agents, Autonomous AI can independently handle customer interactions without requiring human intervention. Autonomous AI acts as a fully capable, virtual agent capable of resolving complex requests, completing transactions, and delivering personalized service — all in real-time. This next generation of Voice AI is transforming how businesses handle high call volumes, reducing costs while delivering faster, more accurate service. 💬 Conversational, Human-Like Interactions Unlike traditional IVR systems, Autonomous AI engages in natural, human-like conversations without rigid menu trees or button prompts. Customers can speak in their own words, and the AI agent will: For example: This level of automation significantly reduces operational costs and enhances customer satisfaction. 🔄 Task Execution Across Systems Autonomous AI is not just conversational — it’s actionable. It can directly integrate with: This enables Autonomous AI to complete complex tasks like: No hold times. No transfers. Just fast, efficient resolutions. 💡 Smart Escalation for Complex Cases If a task exceeds the AI agent’s capabilities, it can automatically: This seamless handoff ensures high-quality service without frustrating the customer. 🧠 Continuous Learning and Improvement Like Assistive AI, Autonomous AI continuously learns from customer interactions. Over time, it improves its accuracy, expands its task-handling capabilities, and becomes more effective at resolving complex issues — reducing human intervention further. ✅ Key Benefits of Autonomous AI: Autonomous AI transforms customer service by automating high-volume interactions, allowing human agents to focus on high-value, complex cases. The Power of Voice AI: Assistive + Autonomous Working Together The true power of Voice AI lies in combining Assistive AI and Autonomous AI. Together, they create an optimal balance of automation and human support: Additional Business Benefits of Voice AI 📈 Scalability Without Increasing Costs Voice AI allows businesses to handle thousands of customer calls simultaneously without expanding headcount. This ensures consistent, 24/7 support while keeping operational costs low. 💵 Revenue Growth Through Personalization By analyzing customer history and real-time sentiment, Voice AI can offer: This enables businesses to not only resolve issues but also drive revenue growth. 📊 Data-Driven Insights for Continuous Improvement Voice AI captures and analyzes customer interactions to identify: These insights empower businesses to proactively enhance their products, services, and overall customer experience. 🌐 Enhanced Accessibility for Diverse Customers Voice AI also improves accessibility by enabling voice-based interactions for customers with disabilities or language barriers, ensuring an inclusive support experience. The Future of Customer Service is Voice AI The days of clunky IVR systems and long hold times are over. Voice AI — both Assistive and Autonomous — is revolutionizing customer service by enabling: Forward-thinking businesses that embrace Voice AI now will not only enhance customer experiences but also drive operational efficiency, reduce costs, and increase revenue. ✅ Ready to transform your contact center with Voice AI?Discover how Assistive and Autonomous AI can redefine your customer service — improving satisfaction, reducing costs, and unlocking new growth opportunities. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 15, 2025in Data, Generative AI0 Comments
AI-Driven Salesforce Explained
14Mar

AI-Driven Salesforce Explained

AI-driven Salesforce refers to the integration of Artificial Intelligence (AI) into Salesforce’s Customer Relationship Management (CRM) platform to enhance its capabilities, automate processes, and deliver smarter, data-driven insights. Salesforce has embedded AI into its ecosystem through Salesforce Einstein, its proprietary AI technology. Here’s a breakdown of how AI drives Salesforce: 1. What is AI-Driven Salesforce? AI-driven Salesforce leverages machine learning, natural language processing (NLP), predictive analytics, and automation to help businesses make smarter decisions, improve customer experiences, and streamline operations. It transforms raw data into actionable insights and automates repetitive tasks, enabling teams to focus on strategic activities. 2. Key Features of AI-Driven Salesforce a) Salesforce Einstein Einstein is the AI layer built into Salesforce that powers intelligent features across the platform. Key capabilities include: b) AI-Powered Insights c) Personalization d) Automation e) Predictive Intelligence 3. Benefits of AI-Driven Salesforce a) Enhanced Customer Experience b) Increased Efficiency c) Data-Driven Decision Making d) Improved Sales Performance e) Scalability 4. Use Cases of AI-Driven Salesforce a) Sales b) Marketing c) Customer Service d) Commerce 5. The Future of AI in Salesforce In summary, AI-driven Salesforce empowers businesses to work smarter, not harder, by leveraging data and automation to deliver better customer experiences and drive growth. It’s a game-changer for sales, marketing, service, and beyond! Content updated January 2025. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 14, 2025in Artificial Intelligence, Data, Salesforce, Salesforce Einstein, Technology0 Comments
Salesforce Data Cloud
14Mar

Maximizing Salesforce Data Cloud

Maximizing Salesforce Data Cloud: Post-Implementation Strategies for Long-Term Success The Fastest-Growing Salesforce Product Salesforce Data Cloud is seeing explosive growth, with a 130% year-over-year increase in paid customers. In just one quarter, it processed an astonishing 2.3 quadrillion records—a 147% jump from the previous year. Businesses leveraging Data Cloud have seen a 165% boost in web engagement, with major brands like The Adecco Group, Aston Martin, and FedEx using it to strengthen customer relationships and drive growth. The Power of Data Cloud: Turning Information into Action What makes Salesforce Data Cloud so impactful is its ability to unify vast amounts of data, creating 360-degree customer profiles and transforming insights into action. But unlocking its full potential doesn’t stop at implementation—it requires ongoing optimization to keep your data clean, your systems efficient, and your AI models accurate. Post-Implementation Best Practices for Salesforce Data Cloud Once your Salesforce Data Cloud is up and running, the next step is ensuring long-term performance and business value. Here’s how to optimize and sustain your Data Cloud investment: 1. Maintain Data Integrity with Ongoing Quality Management 2. Optimize System Performance for Speed and Efficiency 3. Drive User Adoption with Tailored Training 4. Strengthen Data Governance and Compliance 5. Proactively Manage and Optimize Integrations 6. Refine Customer Segmentation for More Personalization 7. Keep AI and Predictive Models Up to Date 8. Measure ROI and Optimize for Business Impact 9. Foster a Data-Driven Culture Through Clear Communication 10. Stay Agile with Continuous Innovation and Community Engagement Conclusion: The Real Value of Data Cloud Begins After Implementation Salesforce Data Cloud is at the heart of next-gen customer engagement, but its true impact lies in how well you maintain and optimize it. Success depends on:✅ Involving key stakeholders in ongoing improvements✅ Enforcing strong data governance for security and accuracy✅ Continuously refining processes to adapt to changing business needs By committing to post-implementation optimization, your organization can stay agile, data-driven, and ahead of the competition—ensuring that Salesforce Data Cloud delivers maximum value now and in the future. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 14, 2025in Data, Salesforce, Salesforce Data Cloud0 Comments
13Mar

Why Its Good to be Data-Driven

The Power of Data-Driven Decision Making Success in business hinges on the ability to make informed decisions. Every operational aspect, from minor choices like office furniture selection to critical investments such as multi-million-dollar marketing campaigns, is shaped by a series of interrelated decisions. While instinct and intuition may play a role, most business choices rely on relevant data—covering aspects such as objectives, pricing, technology, and potential risks. However, excess irrelevant data can be just as detrimental as insufficient accurate data. Why Its Good to be Data-Driven organization… The Evolution of Data-Driven Decision Making Organizations that prioritize data-driven strategies rely on accurate, relevant, complete, and timely data. Simply amassing large volumes of information does not equate to better decision-making; companies must democratize data access, ensuring it is available to all employees rather than limited to data analysts. The practice of using data to inform business decisions gained traction in the mid-20th century when researchers identified decision-making as dynamic, complex, and often ambiguous. Early techniques like decision trees and prospect theory emerged in the 1970s alongside computer-aided decision-making models. The 1980s saw the rise of commercial decision support systems, and by the early 21st century, data warehousing and data mining revolutionized analytics. However, without clear governance and organizational policies, these vast data stores often fell short of their potential. Today, the goal of data-driven decision-making is to combine automated decision models with human expertise, creativity, and critical thinking. This approach requires integrating data science with business operations, equipping managers and employees with powerful decision-support tools. Characteristics of a Data-Driven Organization A truly data-driven organization understands the value of its data and maximizes its potential through structured alignment with business objectives. To safeguard and leverage data assets effectively, businesses must implement governance frameworks ensuring compliance with privacy, security, and integrity standards. Key challenges in establishing a data-driven infrastructure include: The Benefits of a Data-Driven Approach Businesses recognize that becoming data-driven requires more than just investing in technology; success depends on strategy and execution. According to KPMG, four critical factors contribute to the success of data-driven initiatives: A data-driven corporate culture accelerates decision-making, enhances employee engagement, and increases overall business value. Integrating ethical considerations into data usage is crucial for mitigating biases and maintaining data integrity. Transitioning to a Data-Driven Business With the rapid advancement of generative AI, data-driven organizations are poised to unlock trillions of dollars in economic value. McKinsey estimates that AI-driven decision-making could add between .6 trillion and .4 trillion annually across key sectors, including customer operations, marketing, software engineering, and R&D. To successfully transition into a data-driven organization, companies must: By embracing a data-driven model, organizations enhance their ability to make automated yet strategically sound decisions. With seamless data integration across CRM, ERP, and business applications, companies empower human decision-makers to apply their expertise to high-quality, actionable insights—driving innovation and competitive advantage in a rapidly evolving marketplace. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 13, 2025in Data, Generative AI, Technology0 Comments
Scaling AI Systems
11Mar

Scaling AI Systems

Scaling AI Systems: How Salesforce Manages 100,000+ AI Requests Per Minute AI adoption is accelerating, but scaling AI systems to handle massive workloads is a challenge few have mastered. At Salesforce, AI infrastructure supports over 100,000 training and metadata requests per minute—a feat that requires precision engineering, robust orchestration, and resilient architecture. So, how does Salesforce achieve this scale? Key Strategies for Scaling AI Systems 1. Intelligent Orchestration to Manage Demand Handling 100,000+ requests per minute means prioritizing workloads efficiently. Salesforce uses intelligent job orchestration to balance AI training and inference tasks across distributed environments. This ensures resources are allocated optimally without overwhelming systems. 2. Scalable and Fault-Tolerant Infrastructure AI workloads are unpredictable. Salesforce has built a fault-tolerant infrastructure that automatically scales up or down based on demand. This dynamic scaling prevents bottlenecks while ensuring high availability. 3. Metadata-Driven AI Pipelines AI models require continuous updates, and metadata management is key. Salesforce’s AI systems track metadata across training pipelines, enabling seamless model updates and performance monitoring. 4. Distributed Compute for Speed and Efficiency Instead of relying on a single processing hub, Salesforce distributes AI workloads across multiple compute nodes. This reduces latency, improves processing speed, and optimizes resource usage across cloud environments. 5. Robust Monitoring and Automation Scaling AI isn’t just about computing power—it requires proactive monitoring. Salesforce leverages automated observability tools to detect anomalies, optimize processing efficiency, and maintain system health in real time. The Future of AI Scalability As enterprises embrace AI-driven automation, the need for scalable, resilient AI infrastructure will only grow. Salesforce’s approach—combining intelligent orchestration, metadata-driven workflows, and distributed computing—sets a blueprint for managing high-volume AI workloads at scale. For organizations looking to scale AI, the lesson is clear: invest in automation, resilience, and intelligent resource management to unlock AI’s full potential. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 11, 2025in Data, Enterprise, Salesforce0 Comments
Prioritize Data Quality
11Mar

Prioritize Data Quality

Prioritize Data Quality: Strengthening Governance for AI and Business Success 86% of analytics and IT leaders agree: AI’s effectiveness depends on the quality of its data inputs. High data quality brings organizations closer to data maturity and AI success—and it all starts with strong data governance. 🔹 92% of analytics and IT leaders say there’s never been a greater need for trustworthy data.📊 The State of Data and Analytics Report, 2023 Building a Strong Data Governance Strategy Data governance is more than compliance—it’s a structured approach to managing data quality, security, and usability. With the right governance in place, teams gain confidence in their data, leading to smarter decision-making and a culture of trust. Follow these six steps to lay the foundation for a successful governance strategy: 1. Align Governance Policies with Business Needs Meet with stakeholders to understand how data is used across teams. Ensure governance policies cover every critical workflow and use case, helping teams get the data they need—accurately and securely. 2. Define What ‘Data Quality’ Means for Your Organization Create a clear framework using these key data quality dimensions: ✅ Completeness: Are all necessary data fields populated?✅ Timeliness: Is data up to date and aligned with business goals?✅ Validity: Does data comply with governance rules and constraints?✅ Usage: How frequently is the data used for reporting and decision-making?✅ Accuracy: Does the data reflect reality, and is it sourced from trusted origins?✅ Consistency: Are data formatting and structure standardized across sources?✅ Reliability: Has data maintained quality and consistency over time? 3. Implement a Robust Quality Control Process Standardized processes—data entry validation, deduplication, cleansing, and archiving—are essential for governance. Leverage AI-powered tools like Tableau CRM Analytics to automate these tasks with built-in data profiling and enrichment features. 4. Schedule Regular Governance Reviews Your business evolves—your governance strategy should too. Establish a review cadence to assess policies, update processes, and address new data challenges. 5. Train Teams on Data Security and Compliance Education is key. Assign role-based security permissions, ensure regulatory compliance, and provide a clear process for reporting data issues (e.g., a dedicated Slack channel or help desk). 6. Define Success with Data Governance KPIs Tracking governance effectiveness is essential. Use these key metrics to measure impact: Metric Example of Smart KPI How to Track Data Quality Improve overall data quality by 4% per quarter. Assign values to frequency, error rates, and data usage. Data Usage Increase customer data-driven decision-making by 30% in 12 months. Measure employee logins, reports accessed, and data utilization. Time-to-Insight Reduce time from customer action → dashboard update to 10 minutes by next quarter. Track time-to-insight vs. benchmarks. Moving Up the Data Maturity Curve A well-governed data strategy leads to: 📈 Higher efficiency in decision-making🚀 More successful AI and analytics initiatives🏆 Competitive advantage through trustworthy data 🔍 “Ascending the data maturity curve unlocks new efficiencies and a competitive edge.”— Funke Bishi, Associate Director, Data and Business Analysis, RBC Capital Markets Take Action: Strengthen Your Data Governance ✅ Define what ‘quality data’ means for your business.✅ Align governance policies with team needs.✅ Use AI-powered tools like Tableau Data Prep for automated cleansing.✅ Train leaders on data best practices and compliance. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 11, 2025in Data, Salesforce0 Comments
11Mar

Salesforce Introduces AgentExchange

Salesforce Introduces AgentExchange: Empowering Businesses with AI-Driven Digital Labor Salesforce has unveiled AgentExchange, a marketplace embedded directly within the Salesforce platform, designed to simplify the way businesses, developers, and partners build, discover, and monetize AI-driven digital labor. This launch extends Salesforce’s Agentforce ecosystem, reinforcing the company’s commitment to accelerating enterprise adoption of advanced, role-based AI agents. Shifting to Role-Based AI AgentExchange arrives as industries shift from task-based automation to standardized, role-based AI agents. These intelligent agents leverage structured metadata to autonomously perform specific business roles, enhancing operational efficiency and scalability. Salesforce joins key industry players like Workday, SAP, and ServiceNow in shaping this emerging landscape. “Agentic AI is transforming the way businesses operate. Traditional software is rigid — it’s clicks, code, and logic. With AI agents, solutions can now observe, reason, and act autonomously, driving digital labor that expands organizational capabilities.” Adam Evans, EVP and GM of Salesforce AI Marketplace Capabilities and Partner Ecosystem AgentExchange launches with over 200 trusted partners, including Google Cloud, DocuSign, Box, and Workday, offering a variety of secure and compliant pre-built components such as: Real-World Use Cases AgentExchange supports solutions across various industries: Metadata-Driven Integration A core feature of AgentExchange is its metadata standardization, which defines agent capabilities, interfaces, and compliance requirements. This metadata-centric approach simplifies integration, reduces deployment complexity, and empowers organizations to adopt AI agents with confidence. Competitive Edge and CRM-Centric Focus Salesforce distinguishes AgentExchange with its strong focus on CRM-related, customer-facing solutions. While competitors like Workday emphasize HR automation and SAP covers broader ERP functions, Salesforce leverages real-time customer data and Slack integration to enhance front-office interactions. This CRM-centric focus equips businesses with tools to improve customer experience management, further differentiating Salesforce in the AI-driven digital labor space. Enabling an Open AI Ecosystem AgentExchange signals Salesforce’s commitment to fostering an open AI ecosystem, empowering partners, developers, and the Agentblazer community to build and monetize AI-driven solutions. The marketplace includes hundreds of vetted solutions designed to accelerate the adoption of intelligent digital labor across industries. Mark Stewart, CEO and President of Goodyear, shared his excitement about the platform: “AgentExchange offers ready-to-use solutions that enhance our speed, efficiency, and customer experience. This innovation aligns perfectly with Goodyear’s focus on delivering value to our customers.” Partner Innovations Key partners are already contributing to AgentExchange with impactful solutions: Driving the Future of Digital Labor Brian Landsman, EVP & GM of Global Business Development & Partnerships at Salesforce, connected AgentExchange to Salesforce’s AppExchange legacy: “When we launched AppExchange in 2005, we unlocked new value for our platform. AgentExchange now builds on that success, inviting partners, startups, and developers to shape the future of digital labor.” AgentExchange empowers organizations to seamlessly adopt trusted AI solutions, enabling developers to leverage partner expertise and integrate industry-specific capabilities directly within Salesforce. This innovative marketplace positions Salesforce as a leader in AI-driven digital transformation, driving efficiency, innovation, and growth in the evolving digital labor economy. Let Tectonic guide you though this exciting new release aimed to get you up and running even faster with Agentforce! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 11, 2025in AgentExchange, Agentic AI, Data, Enterprise, Google, Salesforce0 Comments
Predictive Analytics for Business Potential
10Mar

Predictive Analytics for Business Potential

Maximizing Business Potential with Predictive Analytics Every business generates vast amounts of data daily, yet not all leverage it effectively. Predictive analytics transforms raw data into actionable insights, enabling companies to forecast demand, reduce operational costs, and enhance customer engagement. Whether through AI-powered models, machine learning algorithms, or cloud-based analytics, predictive analytics is reshaping business strategies. Organizations that integrate predictive analytics into decision-making can anticipate challenges, seize new opportunities, and outperform competitors. This insight explores the significance of predictive analytics and how businesses can harness its power to gain a competitive edge. The Expanding Influence of Predictive Analytics Predictive analytics is revolutionizing industries, driving innovation, and transforming data into strategic advantages. Transforming Industries with Predictive Analytics Predictive analytics is now a cornerstone of modern industries, facilitating smarter decision-making through data-driven insights. By analyzing historical data, businesses can forecast trends, anticipate customer behaviors, and optimize operations. Sectors such as healthcare, retail, and finance are leveraging predictive tools to maintain competitiveness. For example: As industries adopt these solutions, predictive analytics continues to redefine efficiency and innovation. Key Trends in Predictive Analytics Adoption The widespread adoption of predictive analytics is fueled by advancements in AI, big data, and machine learning. Businesses are capitalizing on these trends to sharpen their competitive edge. Notable developments include: As predictive analytics tools become more sophisticated, they are becoming integral to business strategies, helping companies unlock untapped potential. Overcoming Implementation Challenges Despite its benefits, implementing predictive analytics poses challenges, such as data silos, integration complexities, and skill gaps. To address these issues, organizations should: By overcoming these barriers, businesses can fully harness predictive analytics to drive efficiency, innovation, and growth. Predictive Data Modeling for Smarter Decision-Making Predictive data modeling transforms raw data into strategic insights, improving forecasting and operational decision-making. Understanding Predictive Data Modeling Predictive data modeling employs statistical techniques and machine learning algorithms to analyze historical data and predict future trends. Its core components include: By applying these models, businesses can refine their strategies with data-backed insights, improving efficiency and competitiveness. The Role of Data Quality in Predictive Accuracy The effectiveness of predictive models depends on data quality. Inconsistent or outdated data can lead to unreliable predictions, affecting decision-making. Key steps to ensure high data quality include: High-quality data enhances predictive models, enabling businesses to make informed, confident decisions. Enhancing Forecasting with Predictive Data Modeling Predictive data modeling improves forecasting accuracy by analyzing historical trends and projecting future outcomes. Benefits include: Retailers optimize inventory, while manufacturers align production with demand fluctuations, demonstrating the strategic value of predictive modeling. Driving Business Growth with Predictive Analytics Why Businesses Should Adopt Predictive Analytics Now In today’s competitive landscape, predictive analytics is essential for staying ahead. By leveraging data, algorithms, and machine learning, businesses can anticipate risks and opportunities, optimizing strategies while reducing uncertainty. Retailers, for instance, use predictive insights to forecast seasonal demand spikes, ensuring optimal stock levels. As AI advances, predictive analytics is more accessible than ever, making now the ideal time for adoption. Enhancing Decision-Making and Efficiency Predictive analytics eliminates guesswork, empowering leaders with data-backed decisions. Benefits include: These advantages drive sustainable growth and competitive advantage across industries. Seamlessly Integrating Predictive Analytics into Business Workflows To maximize impact, predictive analytics must integrate into existing workflows. Steps for successful adoption include: By embedding predictive analytics into workflows, businesses enhance agility and decision-making capabilities. AI-Powered Predictive Analytics for Competitive Advantage Why AI Predictive Analytics is Transformative AI-powered predictive analytics delivers insights beyond traditional methods, processing vast datasets rapidly to identify complex patterns and trends. Applications include: With AI continuously learning and refining predictions, businesses gain a dynamic advantage. Enhancing Accuracy with AI AI refines predictions by analyzing diverse data sources, including text, images, and videos. Examples include: Advanced AI techniques, such as natural language processing and neural networks, ensure businesses derive actionable insights, driving smarter strategies and better results. Machine Learning’s Role in Predictive Analytics Machine learning (ML) is foundational to predictive analytics, continuously improving model accuracy. Examples include: By leveraging ML, businesses enhance their predictive capabilities, ensuring long-term competitive success. Enhancing Enterprise Solutions with Predictive Analytics Transforming SAP Systems with Predictive Analytics SAP systems integrated with predictive analytics unlock actionable insights from vast datasets. Benefits include: By embedding predictive capabilities, SAP users can optimize operations and drive proactive decision-making. Empowering Salesforce with Predictive Insights Salesforce predictive analytics enhances decision-making across marketing, sales, and customer service. Key capabilities include: With Salesforce Einstein, businesses can streamline operations, boost performance, and foster deeper customer engagement. Predictive analytics is a game-changer, reshaping industries, optimizing operations, and unlocking new growth opportunities. Businesses that embrace predictive analytics today will be well-positioned to navigate future challenges and lead in the data-driven economy. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 10, 2025in Data, Enterprise, Salesforce, Salesforce Einstein0 Comments
AI Agents
08Mar

AI Agents in Action: Real-World Applications

The true potential of AI agents lies in their practical use across industries. Let’s explore how different sectors are leveraging AI agents to solve real challenges. Software Development The shift from simple code completion to autonomous software development highlights AI’s expanding role in engineering. While GitHub Copilot introduced real-time coding assistance in 2021, today’s AI agents—like Devin—can manage end-to-end development, from setting up environments to deployment. Multi-agent frameworks, such as MetaGPT, showcase how specialized AI agents collaborate effectively: While AI agents lack human limitations, this shift raises fundamental questions about development practices shaped over decades. AI excels at tasks like prototyping and automated testing, but the true opportunity lies in rethinking software development itself—not just making existing processes faster. This transformation is already affecting hiring trends. Salesforce, for example, announced it will not hire new software engineers in 2025, citing a 30% productivity increase from AI-driven development. Meanwhile, Meta CEO Mark Zuckerberg predicts that by 2025, AI will reach the level of mid-level software engineers, capable of generating production-ready code. However, real-world tests highlight limitations. While Devin performs well on isolated tasks like API integrations, it struggles with complex development projects. In one evaluation, Devin successfully completed only 3 out of 20 full-stack tasks. In contrast, developer-driven workflows using tools like Cursor have proven more reliable, suggesting that AI agents are best used as collaborators rather than full replacements. Customer Service The evolution from basic chatbots to sophisticated AI service agents marks one of the most successful AI deployments to date. Research by Sierra shows that modern AI agents can handle complex tasks—such as flight rebookings and multi-step refunds—previously requiring multiple human agents, all while maintaining natural conversation flow. Key capabilities include: However, challenges remain, particularly in handling policy exceptions and emotionally sensitive situations. Many companies address this by limiting AI agents to approved knowledge sources and implementing clear escalation protocols. The most effective approach in production environments has been a hybrid model, where AI agents handle routine tasks and escalate complex cases to human staff. Sales & Marketing AI agents are now playing a critical role in structured sales and marketing workflows, such as lead qualification, meeting scheduling, and campaign analytics. These agents integrate seamlessly with CRM platforms and communication tools while adhering to business rules. For example, Salesforce’s Agentforce processes customer interactions, maintains conversation history, and escalates complex inquiries when necessary. 1. Sales Development 2. Marketing Operations Core capabilities: However, implementing AI in sales and marketing presents challenges: A hybrid approach—where AI manages routine tasks and data-driven decisions while humans focus on relationship-building and strategy—has proven most effective. Legal Services AI agents are also transforming the legal industry by processing complex documents and maintaining compliance across jurisdictions. Systems like Harvey can break down multi-month projects, such as S-1 filings, into structured workflows while ensuring regulatory compliance. Key capabilities: However, AI-assisted legal work faces significant challenges. Validation and liability remain critical concerns—AI-generated outputs require human review, and the legal responsibility for AI-assisted decisions is still unresolved. While AI excels at document processing and legal research, strategic decisions remain firmly in human hands. Final Thoughts Across industries, AI agents are proving their value in automation, efficiency, and data-driven decision-making. However, fully autonomous systems are not yet replacing human expertise—instead, the most successful implementations involve AI-human collaboration, where agents handle repetitive tasks while humans oversee complex decision-making. As AI technology continues to evolve, businesses must strike the right balance between automation, control, and human oversight to maximize its potential. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 8, 2025in Data, Technology0 Comments
Aligning Strategy and Goals
07Mar

Aligning Strategy and Goals

Aligning Strategy and Goals: Bridging the Gap Between Data and Business Success Aligning data strategy with business goals is critical—but easier said than done. 41% of business leaders report that their data strategy is only partially or not at all aligned with their objectives. Here’s how to close the gap and make data a true driver of business success. 1. Define Your Business Goals Collaboration between business and IT stakeholders is essential. Start by identifying and prioritizing objectives that drive success, such as revenue growth, customer satisfaction, cost reduction, and market expansion. Business Goal How Data Supports It Revenue Growth Use analytics to identify high-value customers and optimize marketing strategies for higher conversions. Customer Satisfaction Leverage trusted customer data to personalize experiences and improve engagement. Cost Reduction Analyze operational data to streamline processes and improve efficiency. Market Expansion Use market and customer insights to identify new growth opportunities. 2. Determine Key Metrics Once goals are clear, define key performance indicators (KPIs) to measure progress. Business Goal Key Metric Revenue Growth Conversion Rate: Measures the percentage of leads converted into paying customers. Customer Satisfaction Retention Rate: Tracks the percentage of returning customers over time. Cost Reduction Operational Efficiency Ratio: Compares operational costs to revenue. Market Expansion Customer Acquisition Rate: Measures the rate of new customer growth. 3. Assess Resources and Budget Evaluate whether you have the systems, tools, and budget needed to support your goals. If customer personalization is a priority, you may need solutions like Data Cloud to unify and leverage customer insights. A strong CRM or data analytics platform may also be required to track specific KPIs. 4. Build a Data-Driven Culture Data maturity is not just about tools—it’s about people. Empower teams with the skills, training, and mindset to leverage data effectively. Change management initiatives and ongoing education will help integrate data into daily decision-making. See how F5 is building a data-driven culture with Tableau:“Data has been transforming our corporate culture right before our eyes. Every day, I wake up learning something new about data.”— Amie Bright, Former RVP of Enterprise Data Strategy and Insights, F5 5. Align Teams for Success Use this handy checklist to ensure alignment across your organization: ✅ Collaborate with business and IT teams to define and prioritize objectives.✅ Develop key data KPIs in partnership with internal stakeholders.✅ Survey team leaders to assess the tools, systems, and budgets needed.✅ Invest in training and change management to build a data-driven culture.✅ Join a data leadership community to gain insights and best practices. Want to accelerate your data strategy? Reach out to Tectonic to get started today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 7, 2025in Data, Enterprise, Salesforce Data Cloud, Technology0 Comments

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